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# Copyright 2025 The HuggingFace Inc. team and Google LLC. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from collections.abc import Callable
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ...activations import ACT2FN
from ...generation import CompileConfig, GenerationMixin
from ...integrations import use_kernel_func_from_hub, use_kernelized_func
from ...masking_utils import create_bidirectional_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutput
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from ..auto import AutoModel
from .configuration_lasr import LasrCTCConfig, LasrEncoderConfig


class LasrEncoderSubsampling(nn.Module):
    def __init__(self, config: LasrEncoderConfig):
        super().__init__()
        self.dense_0 = nn.Linear(config.num_mel_bins, config.hidden_size)
        self.conv_0 = nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size=config.subsampling_conv_kernel_size,
            stride=config.subsampling_conv_stride,
        )
        self.conv_1 = nn.Conv1d(
            config.hidden_size,
            config.subsampling_conv_channels,
            kernel_size=config.subsampling_conv_kernel_size,
            stride=config.subsampling_conv_stride,
        )
        self.dense_1 = nn.Linear(config.subsampling_conv_channels, config.hidden_size)
        self.act_fn = nn.ReLU()

    def forward(self, input_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.act_fn(self.dense_0(input_features))
        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.act_fn(self.conv_0(hidden_states))
        hidden_states = self.act_fn(self.conv_1(hidden_states))
        hidden_states = hidden_states.transpose(1, 2)
        return self.dense_1(hidden_states)


class LasrEncoderRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: LasrEncoderConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: LasrEncoderConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class LasrEncoderAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: LasrEncoderConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = False

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class LasrEncoderConvolutionModule(nn.Module):
    def __init__(self, config: LasrEncoderConfig, module_config=None):
        """
        Args:
            config (LasrEncoderConfig): Configuration for the model.
            module_config (dict): Configuration for the module (e.g., encoder or decoder).
        """
        super().__init__()
        channels = config.hidden_size
        # kernel_size should be an odd number for 'SAME' padding
        if module_config is None:
            # e.g. using `LasrEncoderEncoderConfig` in src/transformers/models/lasr_encoder/configuration_lasr_encoder.py
            kernel_size = config.conv_kernel_size
            self.activation = ACT2FN[getattr(config, "hidden_act", "silu")]
        else:
            kernel_size = module_config["kernel_size"]
            self.activation = ACT2FN[module_config.get("activation", "silu")]
        self.padding = "same"
        self.pointwise_conv1 = nn.Conv1d(
            channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
        )
        self.depthwise_conv = nn.Conv1d(
            channels,
            channels,
            kernel_size,
            stride=1,
            padding=self.padding,
            groups=channels,
            bias=config.convolution_bias,
        )
        self.norm = nn.BatchNorm1d(config.hidden_size, momentum=config.batch_norm_momentum)
        self.pointwise_conv2 = nn.Conv1d(
            channels, channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
        )

    def forward(self, hidden_states, attention_mask=None):
        """
        Compute convolution module.

        Args:
            hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor.
            attention_mask (`torch.Tensor` of shape `(batch, 1, time, time)`): Attention mask.

        Returns:
            `torch.Tensor`: Output tensor of shape `(batch, time, channels)`.

        """
        # exchange the temporal dimension and the feature dimension
        hidden_states = hidden_states.transpose(1, 2)

        # GLU mechanism, (batch_size, 2*channel, dim)
        hidden_states = self.pointwise_conv1(hidden_states)
        # (batch_size, channel, dim)
        hidden_states = nn.functional.glu(hidden_states, dim=1)

        # Apply padding mask before convolution
        if attention_mask is not None:
            if attention_mask.dtype == torch.bool:
                all_masked_rows = torch.all(~attention_mask, dim=2)
            else:
                all_masked_rows = torch.all(~(attention_mask == 0.0), dim=2)
            hidden_states = hidden_states.masked_fill(all_masked_rows, 0.0)

        # 1D Depthwise Conv
        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.pointwise_conv2(hidden_states)

        return hidden_states.transpose(1, 2)


class LasrEncoderFeedForward(nn.Module):
    def __init__(self, config: LasrEncoderConfig):
        super().__init__()
        self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
        self.activation = ACT2FN[config.hidden_act]
        self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias)
        self.activation_dropout = config.activation_dropout

    def forward(self, hidden_states):
        hidden_states = self.activation(self.linear1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.linear2(hidden_states)
        return hidden_states


class LasrEncoderBlock(GradientCheckpointingLayer):
    def __init__(self, config: LasrEncoderConfig, layer_idx: int):
        super().__init__()
        self.gradient_checkpointing = False

        self.feed_forward1 = LasrEncoderFeedForward(config)
        self.self_attn = LasrEncoderAttention(config, layer_idx)
        self.conv = LasrEncoderConvolutionModule(config)
        self.feed_forward2 = LasrEncoderFeedForward(config)

        self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_self_att = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_conv = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_out = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)

        self.feed_forward_residual_weights = config.feed_forward_residual_weights
        self.conv_residual_weights = config.conv_residual_weights

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_embeddings: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states))
        hidden_states = (
            self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
        )

        normalized_hidden_states = self.norm_self_att(hidden_states)
        attn_output, _ = self.self_attn(
            hidden_states=normalized_hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = hidden_states + attn_output

        conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask)
        hidden_states = self.conv_residual_weights[0] * hidden_states + self.conv_residual_weights[1] * conv_output

        residual = hidden_states
        hidden_states = self.feed_forward2(self.norm_feed_forward2(hidden_states))
        hidden_states = (
            self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
        )

        hidden_states = self.norm_out(hidden_states)

        return hidden_states


@auto_docstring
class LasrPreTrainedModel(PreTrainedModel):
    config: LasrCTCConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    input_modalities = "audio"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LasrEncoderBlock"]
    _supports_flat_attention_mask = True
    _supports_sdpa = True
    # padding is incompatible with flex attention as the resulting mask cannot be used to apply padding
    _supports_flex_attn = False

    # TODO: @eustlb, add support when flash attention supports custom attention bias
    _supports_flash_attn = False

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": LasrEncoderBlock,
        "attentions": LasrEncoderAttention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)

    def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
        encoder_config = self.config.encoder_config if isinstance(self.config, LasrCTCConfig) else self.config
        kernel_size = encoder_config.subsampling_conv_kernel_size
        stride = encoder_config.subsampling_conv_stride

        num_layers = 2
        for _ in range(num_layers):
            input_lengths = (input_lengths - kernel_size) // stride + 1

        return input_lengths

    def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = None):
        """
        Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful
        when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded)
        """
        output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
        # Use target_length if provided, otherwise use max length in batch
        max_length = target_length if target_length is not None else output_lengths.max()
        attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None]
        return attention_mask


@auto_docstring(
    custom_intro="""
    Extends [~modeling_outputs.BaseModelOutputWithPooling] to include the output attention mask since sequence length
    is not preserved in the model's forward.
    """
)
@dataclass
class LasrEncoderModelOutput(BaseModelOutputWithPooling):
    r"""
    attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
        Mask to avoid performing attention on padding token indices after sequence compression. Returned because the
        sequence length may differ from the input sequence length. Mask values selected in `[0, 1]`:

        - 1 for tokens that are **not masked**,
        - 0 for tokens that are **masked**.
    """

    attention_mask: torch.Tensor | None = None


@auto_docstring(
    custom_intro="""
    The LasrEncoder model, based on the Conformer architecture](https://arxiv.org/abs/2005.08100).
    """
)
class LasrEncoder(LasrPreTrainedModel):
    config: LasrEncoderConfig
    base_model_prefix = "encoder"

    def __init__(self, config: LasrEncoderConfig):
        super().__init__(config)
        self.gradient_checkpointing = False

        self.dropout = config.dropout
        self.dropout_positions = config.dropout_positions
        self.layerdrop = config.layerdrop

        self.subsampler = LasrEncoderSubsampling(config)
        self.rotary_emb = LasrEncoderRotaryEmbedding(config)
        self.layers = nn.ModuleList(
            [LasrEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.out_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)

        self.post_init()

    @auto_docstring
    @merge_with_config_defaults
    @capture_outputs
    @can_return_tuple
    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        output_attention_mask: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> LasrEncoderModelOutput:
        r"""
        output_attention_mask (`bool`, *optional*):
            Whether to return the output attention mask.

        Example:

        ```python
        >>> from transformers import AutoProcessor, LasrEncoder
        >>> from datasets import load_dataset, Audio

        >>> model_id = "google/medasr"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> encoder = ParakeetEncoder.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"])
        >>> encoder_outputs = encoder(**inputs)

        >>> print(encoder_outputs.last_hidden_state.shape)
        ```
        """

        hidden_states = self.subsampler(input_features)
        cos, sin = self.rotary_emb(
            hidden_states, torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        cos = nn.functional.dropout(cos, p=self.dropout_positions, training=self.training)
        sin = nn.functional.dropout(sin, p=self.dropout_positions, training=self.training)

        output_mask = None
        if attention_mask is not None:
            output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
            attention_mask = output_mask

        attention_mask = create_bidirectional_mask(
            config=self.config,
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
        )

        for encoder_layer in self.layers:
            # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            if not to_drop:
                hidden_states = encoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_embeddings=(cos, sin),
                    **kwargs,
                )

        hidden_states = self.out_norm(hidden_states)

        return LasrEncoderModelOutput(
            last_hidden_state=hidden_states,
            attention_mask=output_mask.int() if output_attention_mask and output_mask is not None else None,
        )


@dataclass
class LasrCTCGenerateOutput(ModelOutput):
    """
    Outputs of Lasr CTC model generation.

    Args:
        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
            if all batches finished early due to the `eos_token_id`.
        logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
            Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
    """

    sequences: torch.LongTensor
    logits: tuple[torch.FloatTensor] | None = None
    attentions: tuple[tuple[torch.FloatTensor]] | None = None
    hidden_states: tuple[tuple[torch.FloatTensor]] | None = None


@auto_docstring(
    custom_intro="""
    Lasr Encoder with a Connectionist Temporal Classification (CTC) head.
    """
)
class LasrForCTC(LasrPreTrainedModel, GenerationMixin):
    config: LasrCTCConfig

    def __init__(self, config: LasrCTCConfig):
        super().__init__(config)
        self.encoder = AutoModel.from_config(config.encoder_config)
        # Conv rather than linear to be consistent with NeMO decoding layer
        self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)

        self.post_init()

    @auto_docstring
    @can_return_tuple
    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutput:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, LasrForCTC
        >>> from datasets import load_dataset, Audio

        >>> model_id = "nvidia/lasr-ctc-1.1b"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = LasrForCTC.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
        >>> outputs = model(**inputs)

        >>> print(outputs.loss)
        ```"""

        if labels is not None:
            kwargs.setdefault("output_attention_mask", True)
        encoder_outputs = self.encoder(
            input_features=input_features,
            attention_mask=attention_mask,
            **kwargs,
        )

        hidden_states = encoder_outputs.last_hidden_state
        logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2)

        loss = None
        if labels is not None:
            encoder_lengths = encoder_outputs.attention_mask.sum(-1)

            # assuming that padded tokens are filled with pad_token_id when not being attended to
            labels_mask = labels != self.config.pad_token_id
            target_lengths = labels_mask.sum(-1)
            flattened_targets = labels.masked_select(labels_mask)

            # ctc_loss doesn't support fp16
            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)

            with torch.backends.cudnn.flags(enabled=False):
                loss = nn.functional.ctc_loss(
                    log_probs,
                    flattened_targets,
                    encoder_lengths,
                    target_lengths,
                    blank=self.config.pad_token_id,
                    reduction=self.config.ctc_loss_reduction,
                    zero_infinity=self.config.ctc_zero_infinity,
                )

        return CausalLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

    @torch.no_grad()
    def generate(
        self,
        input_features: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        return_dict_in_generate: bool = False,
        compile_config: CompileConfig | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> LasrCTCGenerateOutput | torch.LongTensor:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, LasrForCTC
        >>> from datasets import load_dataset, Audio

        >>> model_id = "google/medasr"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = LasrForCTC.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
        >>> predicted_ids = model.generate(**inputs)
        >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

        >>> print(transcription)
        ```
        """
        model_forward = self.get_compiled_call(compile_config) if compile_config is not None else self.__call__

        kwargs["return_dict"] = True
        outputs: CausalLMOutput = model_forward(
            input_features=input_features,
            attention_mask=attention_mask,
            **kwargs,
        )

        # greedy decoding
        sequences = outputs.logits.argmax(dim=-1)

        # mask out padded tokens
        if attention_mask is not None:
            attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1])
            sequences[~attention_mask] = self.config.pad_token_id

        if return_dict_in_generate:
            return LasrCTCGenerateOutput(
                sequences=sequences,
                logits=outputs.logits,
                attentions=outputs.attentions,
                hidden_states=outputs.hidden_states,
            )

        return sequences


__all__ = ["LasrForCTC", "LasrEncoder", "LasrPreTrainedModel"]
